Syllabus for |
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MVE550 - Stochastic processes and Bayesian inference
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| Stokastiska processer och Bayesiansk inferens |
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| Syllabus adopted 2019-02-22 by Head of Programme (or corresponding) |
| Owner: MPENM |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Major subject: Mathematics
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Department: 11 - MATHEMATICAL SCIENCES
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Course round 1
Teaching language: English
Application code: 20132
Open for exchange students: Yes
Block schedule:
B
| Module |
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Credit distribution |
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Examination dates |
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0118 |
Examination |
6,0 c |
Grading: TH |
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6,0 c
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09 Jan 2021 am J
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09 Apr 2021 am J, |
23 Aug 2021 am J
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| 0218 |
Written and oral assignments |
1,5 c |
Grading: UG |
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1,5 c
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In programs
TKTEM ENGINEERING MATHEMATICS, Year 2 (compulsory)
TKITE SOFTWARE ENGINEERING, Year 3 (elective)
TKTFY ENGINEERING PHYSICS, Year 3 (elective)
MPDSC DATA SCIENCE AND AI, MSC PROGR, Year 1 (compulsory)
Examiner:
Petter Mostad
Go to Course Homepage
Course round 2
Teaching language: English
Application code: 99234
Open for exchange students: No
Maximum participants: 20
Only students with the course round in the programme plan
| Module |
|
Credit distribution |
|
Examination dates |
|
Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0118 |
Examination |
6,0 c |
Grading: TH |
|
|
6,0 c
|
|
|
|
|
|
|
| 0218 |
Written and oral assignments |
1,5 c |
Grading: UG |
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1,5 c
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Examiner:
Petter Mostad
Go to Course Homepage
Eligibility
General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
A basic course in mathematical statistics
Aim
Building on a first course in mathematical statistics, this course is meant to give knowledge about a larger inventory of stochastic models, in particular stochastic processes, and greater knowledge about Bayesian inference, in particular in the context of these models. Together, this knowledge should give a solid basis for practical application and prediction using stochastic processes in connection with data analysis, and for further studies in statistics and probability theory.
Learning outcomes (after completion of the course the student should be able to)
After completion of the course the student should be able to use certain basic stochastic processes as models for real phenomena, and adapt these models using observed data. The student should be able to make predictions from the models, both using theoretical properties and using computer based simulation. The student should be able to make computer based inference using MCMC for certain simple models, and in general understand and apply the Bayesian inferential paradigm.
Content
Discrete time Markov chains. Branching processes. Basic principles for Bayesian inference, using discretization and conjugate priors. Hidden Markov models (HMM). Monte Carlo integration and Markov chain Monte Carlo (MCMC) simulation. Poisson processes. Time-continuous Markov chains. Introduction to Brownian motion.
Organisation
Lectures and excercise classes. Obligatory assignments.
Literature
Dobrow: Introduction to Stochastic Processes with R. (Available at Chalmers as e-book). Wiley 2016.
Lecture Notes.
Examination including compulsory elements
Written exam. Obligatory assignments.